Large deviations, Basic information theorem for fitness preferential attachment random networks

2013/12/31 by K. Doku-Amponsah, F. O. Mettle, T. Narh-Ansah · 1 citation
Computer Science · Mathematics · #cs.IT #cs.SI #math.IT #math.PR #msc:60F10 #msc:05C80 #msc:4A15 #msc:94A24

paper · pdf

published as International Journal of Statistics and Probability,Vol 3, No.2, 101-109(2014) · 11 pages

arxiv created 2014/03/30 · arxiv updated 2014/06/13

Abstract

For fitness preferential attachment random networks, we define the empirical degree and pair measure, which counts the number of vertices of a given degree and the number of edges with given fits, and the sample path empirical degree distribution. For the empirical degree and pair distribution for the fitness preferential attachment random networks, we find a large deviation upper bound. From this result we obtain a weak law of large numbers for the empirical degree and pair distribution, and the basic information theorem or an asymptotic equipartition property for fitness preferential attachment random networks.

Cited by

• Large deviation principle for the empirical degree measure of preferential attachment random graphs

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